We demonstrate improvements over previous models on perplexity and downstream tasks, and offer qualitative analysis of learned discursive patterns.

for this analysis, we specifically focus on DDTM trained with 64 bits total.


The key idea is to explicitly capture the relationship between a target example and different source domains.

d is typically designed as a parameterized classifier in domain adversarial networks (Ganin et al., 2016;Zhang et al., 2017), which is trained jointly with the encoder and the classifiers through a minimax game.


Figure 1: Architecture of the MoE model.

even in this scenario, our approach still performs significantly better, demonstrating its robustness in handling negative transfer.


Compared with No-Target, our model introduces target label information into predicting sentiment label and improves about 0.66%, 1.44% in TSA and 0.59%, 0.91% in DT on both datasets.

these works only take pre-defined features into account and can not find new features.


Finally, as a final (and perhaps thoughtprovoking) finding, the highest attention weights associated to the emoji were given to the words game, boys and football, in that order.

standard attention mechanisms only tell us which text fragments are considered impor- Figure 1: A classic attention network (top), and our attentive label-wise network (bottom), with a specific attention module for each label.


, where s tree p is the hidden state of rule RNN that generated the CFG rule above the current terminal; w t 1 is the embedding of the word generated at time step t 1; and c t 1 is the attention context computed at the previous word RNN time step t 1.   , where W varies depending on whether a rule or a subword unit is needed.

it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT.


For Zh-En, we set the vocabulary size of both sides to 30K, and for En-De, we adopted 50K BPE operations (Sennrich et al., 2016).

given a method to find mergeable states, we can employ recombination in NMT decoding as well.


As for decoding speed, the one of beam-size 6 can be more than twice of the one of beam-size 12 (over 200 tokens/second vs. around 100 tokens/second).

in PBSMT, different sequences could lead to states with identical features based on n-gram suffix, and these states are exactly equivalent.


Ordinary beam search only prunes candidates based on beam size (Lines 15-16), while the proposed method adds a merger to prune extra equivalent states (Lines 6-10).

many of the NMT output sequences are quite similar and they typically contain only local differences that do not influence future decoding significantly.


We note that the availability of document-level context still has a strong impact in the fluency condition (Section 3).

we thank Xin Sennrich for her help with the analysis of translation errors.


(2016) note that their raters "[did] not necessarily fully understand each randomly sampled sentence sufficiently" because it was provided with no context.

when evaluating entire doc- Preference (%) Sentence (N=208) Document (N=200) Figure 1: Raters prefer human translation more strongly in entire documents.


Many works in NLP mainly focus on the zero-shot/semisupervised scenario (Xie et al., 2016;Ma et al., 2016;Carlson et al., 2009), which incorporate extra information to classify objects never appearing in the training sets.

for the first step, We use Wikipedia as the corpus ii and Wikidata as the KB.


Regarding the entire input corpus as one sequence instead of a batch also significantly increases the time complexity of the model.

our implementation is also available online for reproducing the results reported in this paper 2 .


This difficulty makes the co-reference resolution model either prediction a wrong antecedent mention, or cannot find any co-reference.

with the proposed context gate, ASL takes knowledge from context sentences if local inputs are not informative enough.


We then concatenate the outputs of the forward and backward LSTMs in the second layer as the word representations for coreference prediction.

a word representation model cannot learn an informative embedding of a pronoun without considering cross-sentence dependency in this case.


Future work includes relation discovery, which is to identify new relations besides hypernyms in an unsupervised manner.

recently, researchers have started working on extracting word relations based on pre-trained word embedding without the need of an existing corpus, thanks to the success of distributional word representation models such as GloVe (Pennington et al., 2014).


We prove that WRAE is effective against pollution.

it is possible that ground truth unseen hypernyms are be very close to some seen hypernyms in Y train in real cases.


Using relevance scores, our method projects word vectors into the subspace that better reflects the target knowledge.

's vocabulary, we chose properties that were closely related to Binder et al.


by finding a word vector w that is closest to w A − w B + w C in the embedding space.

it is worth noting that our approach is general and can be applied to other embedding models and attributes.


3 Our proposed gender neutralizing scheme is to reserve the gender feature, known as "protected attribute" into w (g) .

for instance, the gender pronoun "he" is replaced by "she" in the aforementioned example.


Aiming at robust classification, we retrieved 500 sentences containing each test concept from the corpus.

we extracted 1,040 potentially abstract unigram wikipedia titles suffixed by either of the two (the positive class).


This phenomena motivated our use TF-IDF for weighing of too common words.

for example, (d) matches both the music and the fish senses: (d) Bass scales are the worst.


This removes unconditionally probable (frequent) words from the top LM predictions.

each representative is a set of size 2 , containing samples from the forward distribution and samples from the backward distribution.


This method could match or outperform existing models that learns generic embeddings in an unsupervised setting, often requiring several days or weeks to train .

the use of gating for selecting word representations has been considered in previous work.


The major benefit of our proposal is that the proposed loss functions can be plugged directly into any NLP model that generates text word-by-word and that may benefit from more diverse output.

we investigated the effects of (a) varying the number N of word neighbors used to compute the loss function, and (b) removing the normalization factor by getting rid of the denominator in Eq.


To achieve this we augment single-task models (ST) 1 1 We replicate models which perform at or close to the with an additional classifier to predict semantic tags and jointly optimize for both the original task and the auxiliary semantic tagging task.

each experiment is run five times, and the average of the five runs is reported.


The transformation of our input vector x into a shared hidden layer h S is described by Equation 3: • Partially shared network (PSN): A subset of hidden layers is shared among the tasks; each task has at least one private hidden layer and a separate output layer.

a layer h X which is shared between the main task and the auxiliary task is split into two subspaces: a shared subspace h X S and a private subspace h X P .


We formulate the variational posterior as a multivariate Gaussian with diagonal covariance.

an issue with the approach described so far is the number of parameters grows linearly in the size of the vocabulary and in the number of covariate partitions, i.e., O(|V | × |C|), where C is the set of all partitions.


Then, the variational lowerbound is defined as where θ (m) is the m-th sample from the variational posterior q (Blundell et al., 2015) via the Gaussian reparametrization in Kingma and Welling (2013).

a noticeable omission in their model is that they do not explicitly model parameter covariance or uncertainty.


We implemented a set of features originally proposed by Evans (2001) and subsequently used extensively in the studies presented in Section 2.

with the extensive research in NLP, very little is known about the way humans approach the disambiguation of it.


These results have important ramifications for further model development.

furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes.


The critical part of learning to split compound words is to correctly identify the location(s) of the split(s).

then, a morpheme dictionary of Sanskrit words is used with other heuristics to remove infeasible word split combinations.


Moreover, in the experiments, we observe that Chinese particles often attach other words, for example, "的" following adjectives and "了" following verbs.

a great quantities of neural models, e.g., tensor neural network , recursive neural network (Chen et al., 2015a), long-short-term-memory (RNN-LSTM) (Chen et al., 2015b) and convolutional neural network (CNN) (Wang and Xu, 2017), have Figure 1: a Segmental Language Model (SLM) works on y = y 1 y 2 y 3 y 4 with the candidate segmentation y 1 , y 2:3 and y 4 , where y 0 is an additional start symbol which is kept same for all sentences.


Similar to neural language modeling, the goal of segmental language modeling is to learn the joint probability function of the segmented sequences of characters.

in addition, the time complexity is linear to the length of the generated sentence, as we can keep the hidden state of the context encoder RNN and update it when generating new words.


we also used a small but real parallel corpus, based on two 19th century newspaper texts and their hand-edited modern equivalents.


Hawaiian has since defied expectations and experienced the beginnings of a remarkable recovery (Warner, 2001;Wilson and Kamanā, 2001).

the transition emitting the final symbol in the string leads to the sole accepting state.


For example, two non-deterministic transitions introduce an optional long-vowel map (a :ā) and (a : a).

the missionary string aa could map to a'a, 'a'a, 'a'ā, or 'ā'ā.


For the research presented in this paper, we choose two related and relatively simple neural network architectures corresponding roughly to Parikh et al.

as neural network approaches increasingly dominate in performance across many NLP tasks, the notion of random shuffling has become overshadowed by that of computational efficiency.


The model is marginally better than DGEM, a graph-based entailment model proposed by the authors of the SciTail dataset We show significant gains over our base entailment model by using an external knowledge base, which are comparable to the gains achieved by DGEM through the use of hypothesis structure.

few randomly selected examples in the test set between symbolic only, neural only, Ensemble and NSnet inference.


We use a simple neural entailment model, Decomposable Attention (Parikh et al., 2016), one of the state-of-the-art models on the SNLI entailment dataset (Bowman et al., 2015).

figure 2: Neural-symbolic learning in NSnet.


The third question shows a case where the NSnet architecture learns a better combination of the neural and symbolic methods to correctly identify the entailment relation while Ensemble fails to do so.

we found that using only the final predictions from the three modules was not effective.


(2018) proposed a Densely Interactive Inference Network (DIIN) by hierarchically extracting semantic features from interaction space.

to the best of our knowledge, this corpus is the largest manually annotated public Chinese SSEI corpus in the bank domain.


The further analysis of the Bank Corpus will be shown in following sections.

the Quora corpus comes from social network sites.


In context of SRL, we propose to leverage SRLunlabeled data in terms of parse trees.

we hypothesize that by leveraging syntactic structure while training neural SRL models, we may achieve robust performance, especially for low resource scenarios.


Improvements of SRL system via use of syntactic constraints is consistent with other observations (Punyakanok et al., 2008).

lastly, (Peters et al., 2018) proposed ElMo, a deep contextualized word representations, and improved the SOTA English SRl by 3.2 F1-points.


Since our training set is too small to directly learn domain-specific embeddings, we learn a mapping from general pre-trained embeddings.

because the logical schema is designed with fulfillment in mind, no logical form can capture the semantics of these sentences, making it impossible for the parser to generate a correct parse.


For the sentence repre-sentation, this method uses a bi-LSTM with selfattention, trained to predict the semantic predicates, and concatenates the final hidden state from each direction as the sentence representation.

there is also a more subtle scenario in which the test data is drawn from the same domain.


One might attempt self-training (McClosky et al., 2006), where new instances are generated by applying a trained model to unannotated data, using high-confidence predictions as ground truth labels.

one way around this is to use beam search (BS), which checks the likelihood of every possible continuation of a sentence fragment, and maintains a list of the n best translations generated up to the current time-step.


The vectors of the normalization candidates are represented by n ij , where i is the index of the original word in the sentence, and j is the rank of the candidate.

interestingly, gold normalization still performs substantially better compared to automatic normalization.


In the settings without external embeddings, the direct use of normalization (NORM) results in a improvement of approximately 3 LAS points.

moNoise reaches 90% accuracy on the word level for the normalization task for our development data.


Obviously, cropping may cause semantic shifts on a sentence-level.

as images are rotated around a center, we rotate the portable tree fragments around the root of the dependency tree to form a synthetic sentence.


we demonstrate that the level of difficulty for several of these tasks is poorly characterized.


Second, instead of learning an explicit mapping between BPEs in the source and target languages, we define BPE tokens by jointly processing both monolingual corpora.

initialization: We populate the initial phrase tables (from source to target and from target to source) using an inferred bilingual dictionary built from monolingual corpora using the method proposed by .


Some Bernoulli experiments in fact only produced a very small number of clarification question, e.g.

further heat maps demonstrating this can be found in the appendix.


The relevant sense of transparency, as defined by Lipton (2016) ( §3.1.2), pertains to the way in which a specific portion of a model corresponds to a human-understandable construct (which Doshi-Velez and Kim (2017) refer to as a "cognitive chunk").

additional future directions for this line of work include application on other tasks such as sequence modeling and multi-document analysis (NLI, Qa); extension to languages other than English; and adding a human evaluation for examining the level of agreement with our measures.


In addition, this setup serves as an effective diagnostic tool for assessing the utility of adversarial attention distributions: if such distributions are truly alternative, they should be equally useful as guides as their base equivalent, and thus perform comparably.

2 it is no surprise that Jain and Wal-  lace find what they are looking for given this degree of freedom.


In this work we consider the case of correlated tasks, in the sense that knowing the labels for task A provides information on the expected label composition of task B.

neural Classifier Our classification model is a simple 1-layer BiLSTM encoder (a concatenation of the last states of a forward and a backward running LSTMs) followed by a linear-predictor.


At each training step we compute the loss and update the gradient with respect to a different random subset.

we demonstrate the approach on the task of Aspectbased Sentiment classification, where we effectively use a sentence-level sentiment predictor to train accurate aspect-based predictor.


To prevent leaking into the masked word pieces, we adopt the BERT strategy and replace all entity candidates from the selectors with a special [MASK] entity if the candidate mention span overlaps with a masked word piece.

each candidate span m has an associated mention-span vector s e m (computed via eq.


In all three models, the higher the layer, the lower the self-similarity is on average.

given that upper layers are much more contextspecific than lower layers, and given that gPT-2's representations are more context-specific than ELMo and BERT's (see Figure 2), this suggests that the PCs of highly context-specific representations are less effective on traditional benchmarks.


These insights help justify why the use of contextualized representations has led to such significant improvements on many NLP tasks.

static representations are much easier to deploy.


Although ELMo, BERT, and GPT-2 have 2, 12, and 12 hidden layers respectively, we also include the input layer of each contextualizing model as its 0 th layer.

the higher the layer, the more contextspecific the contextualized representations.


we also experiment with two correlation measures: Pearson's (r) and Kendall's rank (τ ) correlation coefficients.


p (ρ,w,m) starts relatively low and gradually increases over time.

to linear regression and Pearson's correlation coefficient, Kendall's tau is non-parametric and resistant to outliers (Kendall, 1948).


First, max and min-pooled vectors consistently outperform mean-pooled vectors when all three representations are compared with Pearson correlation.

in case of mean-pooled vectors, this particular departure from normality can be successfully detected by the Shapiro-Wilk normality test, informing the appropriate choice of the correlation coefficient (Pearson's r or robust rank correlation).


The success metric for the STS tasks is the Pearson correlation between the sentence similarity scores provided by human annotators and the scores generated by a candidate algorithm.

intuition tells us that the means of the max-pooled vectors will be shifted , and Kendall's τ (KND).


A fundamental assumption in game theory is that each player i tries to maximize the value of u i .

it will be interesting to test new sense embedding models based on contextual embeddings.


During the data collection process, a user is asked to follow a pre-specified user goal, but it encourages the user to change its goal during the session and the changed goal is also stored, so the collected dialogs are much closer to reality.

it records informable slots about the constraints from users and requestable slots that indicates what users want to inquiry.


In practice, dialog acts are delexicalized in the dialog policy.

these methods still require some knowledge about user goals or annotations of dialog ratings from real users.


Since the reward estimator of GDPL evaluates state-action pairs, it can always guide the dialog policy during the conversation thus leading the dialog policy to a successful strategy, which also indirectly demonstrates that the reward estimator has learned a reasonable reward at each dialog turn.

the loss functions for the dialog policy and the reward estimator become respectively as follows: where T is the number of dialog turns.


Then we compute matching score g(U i , r i ) based on the matching features.

(2016) perform context-response matching with a multi-view model on both word and utterance levels.


2018, we first encode the dialogue context and use it to recognize the emotional state (n possible emotions).

the first is a multi-task approach that jointly trains a model to predict the current emotional state of the user and generate an appropriate response based on the state (Lubis et al., 2018;Rashkin et al., 2018).


Both cases have succeeded in generating empathetic and emotional responses, but have neglected some crucial points in empathetic dialogue response generation.

moEL achieves the highest  Empathy and Relevance score.


It's noticing that our framework with two methods still outperform KV Net in In-Car dataset on whole BLEU and Entity F metrics, which demonstrates the effectiveness of our framework.

different from typical text generation, the successful conversations for task-oriented dialogue system heavily depend on accurate knowledge base (KB) queries.


Edges: Construction of the edges E depends on the context to be modeled.

emotion recognition in conversation has attracted attention from researchers only in the past few years due to the increase in availability of open-sourced conversational datasets (Chen et al., 2018;Zhou et al., 2018;Poria et al., 2019a).


This makes inter-dependency and selfdependency modeling difficult.

this pooling mechanism does not consider speaker information of the utterances and the relative position of other utterances from the target utterance.


This phenomenon has been observed in a prior study .

the referring process is dynamic and balances between relatedness and affectiveness of the retrieved knowledge entities using a context-aware affective graph attention mechanism.


Discarding early contextual utterances may cause information loss, but this loss is negligible because they only contribute the least amount of information .

the hierarchical self-attention follows two steps: 1) each utterance representation is computed using an utterance-level self-attention layer, and 2) a context representation is computed from M learned utterance representations using a context-level self-attention layer.


Multiple emotion detection from texts has been previously addressed in (Zhou et al., 2016) which predicted multiple emotions with different intensities based on emotion distribution learning.

most these baselines are either reimplemented or cited from published papers.


Moreover, the event-driven attention enables dynamically highlighting important event-related parts evoking the emotions in texts.

the model utilizes the corpus-level event information by a joint attention mechanism to consider global events in corpus, which aggregates the semantic representations h = (h 1 , h 2 , ..., h N ) of an input text obtained and measures the interaction the words in the text with the event representations s = (s 1 , s 2 , ..., s K ) by the event-driven attention.


We need to separate the relevant emotions from irrelevant ones and only predict the ranking results for the relevant emotion labels.

as for event encoder, event representations are produced by the ReLU-actived neural perceptrons taking the event-word weight matrix E ∈ V × K as inputs.


This confirms that Ref2Seq is able to capture user and item content to generate the most relevant content, compared with unpersonalized models such as LexRank and personalized models that do not leverage historical justifications such as Attr2Seq.

34 We further filter those users with fewer than five justifications.


Our user encoder and sequence encoder share the same structure, which includes an embedding layer, a two-layer bidirectional GRU (Cho et al., 2014), and a projection layer.

table 1: to reviews and tips, we seek to automatically generate recommendation justifications that are more concise, concrete, and helpful for decision making.


Then, we feed the representation sequence into a standard LSTM for obtaining a segment representation h ct ∈ R k at each time step t, i.e., h ct = LSTM v ct .

the utterances (u i ) with positive/neutral/negative sentiments are denoted by red/orange/blue boxes.


In addition, we will study other interesting tasks in customer service dialogues, such as outcome prediction or opinion change.

based on a previous survey, over 77% of buyers on Taobao communicated with sellers before placing an order (Gao and Zhang, 2011).


Based on a previous survey, over 77% of buyers on Taobao communicated with sellers before placing an order (Gao and Zhang, 2011).

our model predicts the label "unsatisfied" correctly and also predicts reasonable sentiment polarities for customer utterances.


To our knowledge, we are the first to study dependency forests for medical information extraction, showing their advantages over 1-best tree structures.

dEPTREE achieves similar performance as C-GCN and is slightly worse than C-AGGCN, with one potential reason being that C-AGGCN takes more parameters.


• DEPTREE: Our baseline using 1-best dependency trees, as shown in Section 4.

using dependency forests and parser confidence scores, both KBESTEISNERPS and EDGE-WISEPS obtain significantly higher numbers than DEPTREE.


The state representation of the entire tree consists of all word states: In order to capture non-local interactions between words, the GRN layer adopts a message passing framework that performs iterative information exchange between directly connected words.

for the other hyper-parameters, word embeddings are initialized with the 200-dimensional BioASQ vectors 5 , pretrained on 10M abstracts of biomedical articles, and are fixed during training.


B 3 metric is a standard measure to balance the precision and recall of clustering tasks, and is commonly used in previous OpenRE works (Marcheggiani and Titov, 2016;Elsahar et al., 2017).

the second dataset we use is FewRel-distant, which contains the distantly-supervised data obtained by the authors of FewRel before human an-notation.


Yao (2011) consid-ers OpenRE as a clustering task for extracting triples with new relation types.

we follow the split of FewRel to obtain the auto-labeled train set and unlabeled train set.


In multi-channel attention, feature vectors are first fed into a fully connected neural network to get their hidden representations h i .

the same "entity mask" strategy is used which replaces subject (or object) entity with special NER -SUBJ (or NER -OBJ) tokens to avoid overfittting on specific entities and provide entity type information.


Relation extraction aims to detect the semantic relationship between two entities in a sentence.

the proposed knowledgeattention mechanism is effective in capturing the linguistic clues of relations represented by relation indicators in the relational semantic space.


If we keep updating the model to learn better entity and relation representations, our alignment framework can successfully uncover more entity and relation alignments such as (v 1 , v 4 ) and (r 1 , r 4 ).

here,d is the number of features in each hGCN-output entity embedding and m is the number of features in each relation representation.


This is because aligned entities tend to have some relations in common, and similar relations should have similar categories of head and tail entities.

given a relation r ∈ R a , there is a set of triples of r, where H r and T r are the sets of head entities and tail entities of relation r, respectively.


Inspired by these works, we propose a deep generative model that aims at triplet augmentation for k-shot learning.

during the meta-training phase, the recognition network takes O as input and learns the variational parameters µ θ and σ θ of the variational posterior q θ (z|O), where the latent semantics z is assumed to follow a Gaussian distribution.


Moreover, a meta-learner is further adopted to learn a initial representation of the model that can be easily adapted to unseen relations and entities.

for WDtext, Con-Mask is a strong baseline and has a better result on Hits@10 and Hits@5, but it performs worse on Hits@1 and MRR compared to our framework.


It is to make best use of labeled words via the sequence labeling module and the classification module.

note that we have no training instance for O words.


To make notations consistent, we useỸ = Y {UN,B-NT,I-NT} to denote the label set of WL data, where UN indicates that the word is unlabeled, and NT denote only the type is unlabeled.

wL data is more practical to obtain, since it is difficult for people to accurately annotate those entities that they do not know or are not interested in.


4 We adopt the common part of their models related to 6.2 Results on Low-Resource Languages Table 2 shows the overall performance of our proposed model as well as the baseline methods (P and R denote Precision and Recall).

we also use the nonentity sampling (Section 5.1).


Both approximation methods iteratively improve the global assignment, but are still computationally expensive with unbounded number of iterations.

in this section, we first introduce the architecture of the base models, then present the proposed DCA model under the standard supervised learning framework.


We compare the performance of DCA with or without neighbor entities (i.e., 2-hop vs. 1-hop).

for eachê i ∈ S t , we collect its neighborhood N (ê i ) consisting of Wikipedia entities that have inlinks pointing toê i .


Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration; (3) The discriminative features learned by the discriminator of AEM provide a straightforward way to visualize the extracted events.

long texts such news articles often describe multiple events which clearly violates this assumption; (2) During the inference process of both approaches, the Gibbs sampler needs to compute the conditional posterior distribution and assigns an event for each document.


2) APR (2015 news from AP and Reuters) and Wiki (a subset of English Wikipedia) are two datasets published by Shen et al.

selecting patterns with high instant feedback but low delayed feedback can cause semantic drift problem (Curran et al., 2007), where the later extracted entities belong to other categories.


In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.

the improvement on recall is more substantial: It improves recall relatively by 5.8%.


On BioCFE, we observe that 93.8% statement sentences make multiple tuples: 21.7% of the sentences have at least one token that appears in at least one fact tuple and at least one condition tuple, expecting tags "B/I-fYZ" and "B/I-cYZ"; 18.1% of the sentences have at least one token that appears in one condition tuple as a part of subject and in another condition tuple as a part of object, expecting tags "B/I-c1Z" and "B/I-c3Z".

n s is the length of the sentence s. The loss function of the tuple completion tagging layer is consisted of two parts, loss on fact tuples and loss on condition tuples: where n and m are the number of fact and condition tag sequences for the sentence s, respectively.


Figures 3 and 4 show the learning curves of these supervised models.

regardless of the recent successes in applying cross-lingual transfer learning to sequence labeling tasks, such as name tagging (e.g., (Mayhew et al., 2017;Lin et al., 2018;Huang et al., 2019)), only limited work has explored cross-lingual relation and event structure transfer.


Many Chinese word segmentation errors occur on tokens involved in names.

the hidden representation at the k th layer for the i th node is computed as: where W is the weight vector, b is the bias term, and d i represents the degree of the i th node.


Let B be a finite set, in which each b ∈ B is a pair (e, e ) of entities.

any suitable neural network can be deployed to achieve this goal.


Despite their capability of learning from large scale data, we show in this paper that these DS relation extraction strategies fail to adequately model the characteristic of the noise in the data.

we will denote by θ the set of all parameters {r : r ∈ R} and the parameters for generating encoding x.


To evaluate the effectiveness of our proposed Doc2EDAG, we conduct experiments on a realworld dataset, consisting of large scales of financial announcements.

as Table 3 shows, Doc2EDaG achieves significant improvements over all base- -PathMem -11.2 -0.2 -10.1 -16.3 -10.9 -9.7 -SchSamp -5.3 -4.8 -5.3 -6.6 -3.0 -5.0 -DocEnc -4.7 -1.5 -1.6 -1.1 -1.5 -2.1 -NegCW -1.4 -0.4 -0.7 -1.3 -0.4 -0.8 Table 5: Performance differences of Doc2EDaG variants for all event types and the averaged ones (avg.).


"Poly" and "Mono" correspond to polysemous and monosemous trigger splits.

further, thanks to the usage of sense-level information, triggers could be more precisely classified into correct event types.


(2) To a certain extent, the NPN model could alleviate the problem by utilizing hybrid representation learning and nugget generator in a fix-sized window.

it is worth mentioning that the paths are sense-level, which means all the sense information of words that end in one specific character will flow into the memory cell of the character.


The forward and backward outputs are concatenated to construct character representations: where ← − h c i and − → h c i denote the forward and backward outputs of bidirectional LSTM.

the final representation of entities will be sent into a ReLU activation function and the softmax layer to predict entity categorical labels.


• Layered sequence labeling model will first extract the inner entities (contained by other entities) and feed them into the next layer to extract outer entities.

these methods can be divided into three groups: Finkel and Manning (2009) and Ju et al.


The action is what we generate in each time step.

although they jointly training the entity model and relation model, they ignore the influence between triplets actually.


Once we obtained a triplet, we can compare it with the gold triplets and know if this triplet is good or not.


However, due to automatic generation, the output clusters often contain some incorrectly canonicalized elements.

in CaRe(CN=edge), the model is expected to learn the meaning of two NPs being canonical and encode it in the vector embedding of the new edge type.


We also experimented with more sophisticated attention-based context vector generation and gating mechanism for embedding update stage, but they resulted in a performance drop.

fB15k (Bordes et al., 2013), an ontological KG, has on an average 32 triples for each entity and 360 triples for each relation.


In order to design a solution to mitigate the effects of noisy data, we can treat our DSRE model learning procedure as analogous to two students training with a curriculum to answer a list of multiple-choice questions, where some difficult multiple-choice questions may have no correct answers (false positive).

(2) We propose conflicts and small loss tricks for our collaborative curriculum learning.


We develop a new neural RE method REDS2 in the multi-instance learning paradigm which fuses information from 1-hop DS and 2-hop DS using a hierarchical model structure, and substantially outperforms existing RE methods on a benchmark dataset.

• Sentence Encoding: For each sentence s in bag S h,t or S T h,t , a sentence encoder is used to obtain its semantic representation s. • Hierarchical Bag Aggregation: Once the embedding of each sentence is learned, we first use a sentence-level attention mechanism to get bag representation h and h T , and then aggregate them for final relation prediction.


Since 2-hop DS bag S T is collected indirectly through anchor entity pairs in Web tables, despite that it brings abundant information, it also contains a massive amount of noise.

(2016) use neural networks for joint entity and relation extraction.


The other includes mentions that are in consecutive sentences ("next") for the broader context.

training minimizes binary log loss using all negative examples.


We will provide more details in Section 4.

they built end-toend systems that extract events first and then predict temporal relations between them (Fig.


Prior studies either have a mismatch between training and inference as different routines are followed in the two phases, or compute losses with respect to each individual label and make flat predictions during inference time.

the local per-parent label probabil- where σ denotes the sigmoid function, and C t ∈ R |C(lt)|×C denotes the candidate embeddings of HiLAP-SL, i.e., an embedding matrix consisting of the children of current label l t , rather than all the labels where x i has been placed.


similar to the document word, one label can be represented as an embedding vector and the label set will be encoded by a trainable matrix C ∈ R l×k .


On the one hand, it is possible that different models/methods identify the same set of important features (high similarity) and the performance difference in prediction is due to how different models weigh these important features.

while we focus on consistent patterns across classification tasks, it is certainly interesting to investigate how properties related to tasks and data affect the findings.


Consistent with H3b, adjectives are more important in sentiment classification than in deception detection.

the negative correlation becomes stronger as k grows, indicating that length has a stronger effect on similarity when we consider more top features.


Such a high entropy can contribute to the low similarity between LSTM with attention and other models.

it is reassuring that that similarity between any pairs is above random, with a sizable margin in most cases (BERT on SHAP is an exception; see supplementary materials).


CNN wrongly captures key phrases British Energy and the nuclear generator and thus misclassifies the example into World.


Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.

the Encoder1 serves as a pioneer for providing global information, while the Encoder2 focuses on extracting better local features by incorporating the former into the local extraction procedure.


It includes annotations from 9 annotators on the relevance of 650 reviewer-paper pairs.

all the abstracts of a reviewer are concatenated as one document, which is then used to profile the reviewer.


Suppose that these topic vectors of the reviewer are stacked as a matrix P ∈ R d×K , and those of the submission as Q ∈ R d×K .

reviewer 4 had done extensive research in general graphical models including Bayesian model.


For seen domains, similar to the fluency ratings, both our neural pipeline approaches were rated the highest, whereas UPF-FORGe was considered the most semantic approach in unseen domains.

we compared the proposed approaches with 4 state-of-the-art RDF-to-text systems.


Since the WebNLG corpus only contains single-paragraph texts, this step will be only evaluated on sentence planning, being closer to the Aggregation task of the original architecture (Reiter and Dale, 2000).

in total, our Discourse Ordering data consists of 13,757, 1,730 and 3,839 ordered triple sets for 5,152, 644 and 1,408 training, development and test input triple sets, respectively.


In practice, when treating content selection as latent variables, the model tends to end up with a trivial solution of always selecting all source tokens (Shen et al., 2018a;Ke et al., 2018).

), (2) soft-select (SS), (3) Reinforce-select (RS) and (4) Variational-Reinforce-select (VRS) regarding their performance on content selection.


(2017) to produce a stance score for each passage.

to encode keyphrases in the memory bank M, each keyphrase is first converted into a vector e k by summing up all its words' embeddings from GloVe (Pennington et al., 2014).


The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.

we can compare with their work as fairly as possible.


If h k i is the encoding of the k-th layer for , and y i = h m i where m = 6 is the final BiLSTM layer, while LSTM f and LSTM b are the forward and backward LSTM transformations.

in order to understand the meaning of sentences, the semantics of verbs plays a crucial role, since verbs define the argument structure roughly in terms of "who" did "what" to "whom", with the arguments being the constituents that bear a semantic relation (called semantic role) with the verb.


This can be achieved by leveraging BabelNet (Navigli and Ponzetto, 2010), a lexical-semantic resource that provides multilingual synsets in 284 different languages linked to WordNet itself.

because, in principle, a new linguistic resource might seem to provide an arbitrary contribution, we also provide here an experimental validation of its usefulness in an SRL task.


(2018) proposes an unsupervised model, Sent2Vec, to learn an n-gram feature in a sentence to predict the center word from the surrounding context.

for fair comparison, embeddings models are divided into two categories: non-parameterized and parameterized ones, as described in section 1.


We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions.

we show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions.


Ranking Transformer models are pretrained on Reddit data (Mazaré et al., 2018) and fine-tuned.

we adapt the BERT contextual language model (Devlin et al., 2018) to the task of dialogue in two ways: as a bi-ranker, which is fast and practical as a retrieval model, and as a cross-ranker which is slower at inference time but allows more feature cross-correlation between context and response.


ANNA is a simulation of human assistants who do not necessarily know themselves how to optimally accomplish the agent's goal: they are only familiar with scenes along certain paths in the environment, and thus give advice to help the agent make partial progress.

realistic simulators play an important role in sim-to-real approaches, in which an agent is trained with arbitrarily many samples provided by the simulators, then transferred to real settings using sample-efficient transfer learning techniques (Kalashnikov et al., 2018;Andrychowicz et al., 2018;Karttunen et al., 2019).


We now focus on extrinsic evaluation of the embeddings.

w. Barsalou (1999); Fincher-Kiefer (2001) insist on the intuition that language has to be grounded in physical reality and perceptual experience.


Comparing the different types of annotations, on a high-level, we can see that recall, in particular, varies widely across categories; unsurprisingly perhaps, systems, in general, exhibit higher recall for proper nouns (particularly full and extended names) with direct references.

while much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider.


Most Open IE systems extract binary relations using domain-independent syntactic and lexical constraints.

as the test data from aW-OIE-C follows the same annotation style as the training data, the performance of SenseOIE is much higher on this dataset compared to other datasets.


Rather, it would be more beneficial to apply multiple OpenIE systems and combine the wisdoms of all the systems.

as noted in earlier works Schneider et al., 2017), this method penalizes different but equally valid arguments, which are resulted from different annotation styles employed by different Open IE systems.


The relation extraction component uses features 1-4.

a third option unclear can be chosen in the case of uncertainty.


However, these models are still better at capturing syntax than they are at more entityfocused aspects like coreference (Tenney et al., 2019a,b); moreover, existing state-of-the-art architectures for such tasks often perform well looking at only local entity mentions (Wiseman et al., 2016;Lee et al., 2017;Peters et al., 2017) rather than forming truly global entity representations (Rahman and Ng, 2009;.

this includes the entity specific MRC models, EntNet (Henaff et al., 2017), QRN (Seo et al., 2017), and KG-MRC (Das et al., 2019).


Finally, two pre-trained cross-lingual language models are used to initialize the encoder and decoder respectively, based on which, denoising auto-encoder and iterative back-translation are leveraged to finetune the unsupervised machine translation models.

notice that by doing another iteration ("Iter 2") with updated ngram tables as described in Section 3.4, we further improve the performance a bit for most translation directions with the improvements of en2fr and ro2en bigger than 0.5 BLEU point, which confirms the potential that fine-tuned machine translation models contain more beneficial cross-lingual information than the initial n-gram translation tables, which can be used to enhance the pre-trained model iteratively.


The second one is Next Sentence Prediction, which means to predict whether two sentences are adjacent or not.

also build a hybrid system by combining the best pseudo data that SMT models generate into the training of the NMT model while Ren et al.


LaSyn is able to generate diverse translations that reflect the sentence structure implied by the input POS tags.

as the table shows, empirically, our current implementation incurs m 4.


In terms of complexity, LaSyn only adds a linear term (in POS tag size to the decoding complexity.

many Statistical machine Translation (SmT) approaches have leveraged benefits from modeling syntactic information (Chiang et al., 2009;Huang and Knight, 2006;Shen et al., 2008).


To avoid this, models often resort to greedy search which only allows them to explore a limited portion of the latent space.

its decoding complexity is where B is beam size, m is a constant proportional to the tag set size and N is output size.


Based on the expectation and variance of wordand sentence-level translation probabilities calculated by Monte Carlo Dropout (Gal and Ghahramani, 2016), we introduce various confidence measures.

how to quantify the confidence of model predictions at both word and sentence levels?


We find that the translation performance of SEARCH rises with the increase of monolingual corpus size in the beginning.

the training set consists of 4.47M sentence pairs with 116M English words and 110M German words.


127: When an output capsule c t j receives the incoming messages u t i→j , its state will be updated and the coefficient α t ij is also re-computed for the input capsule h t−1 i .

the concatenation of news-test 2012 and news-test 2013 is used as the validation set and news-test 2014 as the test set.


It is reasonable because, from the view of e i , e j is the most similar entity in KG 2 .

to overcome the heterogeneity and language gap between different KGs, JAPE only considers the range types of attribute values, with the specific attribute values discarded.


(2017) and  pointed out, the existing cross-lingual alignments usually account for a small proportion of the total.

given a relational triple τ = (h, r, t), where r represents a specific relation between the head entity h and the tail entity t, TransE and its variants ex- r and v t denote the embeddings for h, r and t, respectively.


• To the best of our knowledge, GCN-EA (Wang et al., 2018) is the only one method employing non-translation-based embedding technique.

we can easily see our method significantly outperforms all the four baselines, especially with the metric Hist@1, which indicates our method can better capture the semantics of different KGs.


Since the conception of linear mapping between two spaces, there are only very small minor improvement in the supervised mapping function itself and largest improvements are obtained by updating the word translation retrieval in the shared space and normalization steps (Artetxe et al., 2018).

the contributions of this work are three-fold.


Large scale dictionaries are used in many NLP tasks, for example, web data cleaning.

the main success of these methods was obtained on linguistically similar languages where a large volume of monolingual data from compatible sources is available.


If we only use domain-specific data to train the NMT model for such a domain, the performance of resulting model is usually unsatisfying.

more specifically, at the k-th iteration, we first transfer the translation knowledge of the previous in-domain NmT model θ out trained on D out (Line 6), and then reversely transfer the translation knowledge encoded by θ Obviously, during the above procedure, one of important steps is how to transfer the translation knowledge from one domain-specific NmT model to the other one.


The agent tends to focus on learning from the ensemble model rather than exploring its own prediction to converge rapidly.

in this paper, according to the Generate sequence: Compute KD loss: Compute NLL loss: Compute Agent loss: Model loss: 13 Update gradients for each agent; 14 end 15 until convergence; previous experiments, we find that a simple multitask learning technique without sharing any modules presents promising performance, in Algorithm 1, we describe the overall procedure of our approach.


This confirms that training with more agents leads to better translation quality, and reveals the relevance of using multiple agents.

the knowledge distillation from the ensemble model to the agent stems from the better translation generated by the ensemble model.


Note again that, nevertheless, pivoting (second row) is very poor in efficiency since it performs decoding twice with the individual models.

(2018) use the expectation-maximization algorithm with the target sentence as a latent variable.


We conjecture that the cross-lingual encoder needs a lot more data to be fine-tuned for another decoder, where the encoder capacity is basically divided into two languages at the beginning of the fine-tuning.

to optimize the usage of given source-pivot and pivot-target parallel data for the source→target direction, we devise the following techniques to smooth the discrepancy between the pre-trained and final models: • Step-wise pre-training with careful parameter freezing.


It achieves zero-shot performance of 11.5% BLEU in French→German and 6.5% BLEU in German→Czech (newstest2013), while  showing comparable or better fine-tuned performance against plain transfer (see also Table 2).

to other NMT transfer scenarios (Zoph et al., 2016;Nguyen and Chiang, 2017;Kocmi and Bojar, 2018), this principle has no language mismatch between transferor and  Figure 2: Step-wise pre-training.


We introduce phrase interaction approach to better model dependencies between phrase representation.

does the integration of the proposed MG-SA into the state-of-the-art TRANSFORMER improve the translation quality in terms of the BLEU score?


The general trend in NLP is towards increasing model capacity and performance via deeper neural networks.

dS-Init is partially inspired by the Fixup initialization (Zhang et al., 2019).


Existing methods mainly focus on developing novel network architectures so as to stabilize gradient back-propagation, such as the fast-forward connection (Zhou et al., 2016), the linear associative unit (Wang et al., 2017), or gated recurrent network variants (Hochreiter and Schmidhuber, 1997;Gers and Schmidhuber, 2001;Cho et al., 2014;Di Gangi and Federico, 2018).

except for IWSLT14 De-en task, we collect subword units independently on the source and target side of training data.


Results show minor differences in translation quality, but improvements in training and decoding speed, and a reduction in the number of model parameters.

though DCNN achieves encouraging performance on newstest2014, it falls behind the baseline on other test sets.


Riloff et al., 2002;Padó and Lapata, 2009;Xi and Hwa, 2005).

we demonstrate that such annotation can be performed rapidly by untrained L2 (second language) speakers.


In §4 we established that our discriminativelytrained neural aligner outperforms unsupervised baselines, especially on NER spans; in §5 we verified that the alignments it produces can be productively applied a downstream IE task (NER) via dataset projection.

additionally, human annotators outperform Discalign when evaluated on the same sentences.


As shown in Table 3, our approach achieves a better CDR than the Transformer baseline, which means superiority in translation adequacy.

these experiments reveal that our guided dynamic routing is a better choice to model and exploit the dynamic PASt and FUtURE.


These studies mainly use capsule network for information aggregation, where the capsules could have a less interpretable meaning.

interestingly, there is a clear trend that the assignment probabilities to the PAST capsules gradually raise up, while Figure 4: Visualization of the assignment probabilities of iterative routing.


For instance, after the target word "defended" was generated, the assignment probabilities of its source translation "辩护" changed from FUTURE to PAST.

our model learns what we expect by the aid of auxiliary learning signals, which endows our model with better interpretability.


In QE tasks, the edit distance is calculated between a MT hypothesis and its post-edits.

we then evaluate the performance of our proposed MLP model (AlignDet) and two recent models under wMT word-level QE shared-task: • CEQE (Hu et al., 2018) Using a three-part neural network model, which encodes semantic factors, local context and global context successively.


1) illustrates a case where Byers's is missing.

this discovery gives further credence for the usefulness of our proposed adequacy-oriented error detection task.


Attention method does not provide as accurate word importance as the Attribution, which exhibits the superiority of gradient-based methods and consists with the conclusion reported in the previous study (Jain and Wallace, 2019).

there are many possible ways to implement the general idea of exploiting gradients for model interpretation.


Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods.

• We provide empirical support for the design principle of NMT architectures: essential inductive bias (e.g., language characteristics) should be considered for model design.


To be specific, under the setting of five operations and Attribution method, Transformer shows a relative decrease of 55% on BLEU scores while the decline of RNN-Search model is 64%.

in the following analyses, we mainly focus on the POS tags (Table 3) and fertility properties (Table 4).


All the data has been tokenized and segmented into sub-word symbols using Byte Pair Encoding (BPE) (Sennrich et al., 2016).

without loss of generality, we choose hierarchical clustering (Rokach and Maimon, 2005) to cluster the embedding vectors in our experiments.


Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair.

third, language family cannot characterize all the features of a language to fully capture the similarity between languages.


• Second, Embedding outperforms Individual (mostly by more than 1 BLEU score) on 12 out of 23 languages 14 , which demonstrates that clustering can indeed bring performance gains with fewer models, especially for languages with relatively small data size.

there are still several languages on which our clustering methods (Family or Embedding) largely outperform Individual.


Note that this limits the training data only to word forms present in UniMorph, as those are the only ones for which we have a gold tag.

most words in our Slavic dictionaries are nouns and adjectives with much smaller paradigms.


Given a list of N source language word forms x 1 , .

10 the last two employ a self-learning method in which they alternate between a mapping step and a word alignment (dictionary induction) step in an iterative manner.


Third, we devise a training schedule ( §2.3) that substitutes structural biases for attention monotonicity.

in some cases cross lingual transfer should be avoided in favor of a purely monolingual setting (H).


Without the warm-up period and the additional copying tasks, our model's dev accuracy is worse than any of the baselines.

in fact, excluding the languages with no typological information available, there is strong corre-lation (ρ=0.6) between genetic distance and improvement from hallucinated data.


The same could hold for transfer for the central Asian languages (Tatar, Turkmen, Azeri among others) which use a variety of the Latin, Cyrillic, or Arabic scripts.

the model of Wu and Cotterell (2019) requires a dynamic programming method that forces strict monotonicity, with an additional (non-parallelizable) computational cost O(|x| 2 ) throughout training.


In this setting, our method is equal to Tiedemann and Agić (2016) where the only difference the our baseline parsing model.

experiments on University Dependency Treebanks show that code-mixed treebanks are more effective than translated treebanks, giving highly competitive performances among cross-lingual parsing methods.


This decoder state represents the state after all its children are generated.

3 The encoder is a 5-layer bidirectional RNN based on Gated Recurrent Units (BiGRU) (Cho et al., 2014).


* Linlin Liu is under the Joint PhD Program between Alibaba and Nanyang Technological University.

the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures.


Most comparisons involve neural systems which are based on BiLSTMs He et al., 2018b; or use neural networks for learning SLR-specific embeddings (FitzGerald et al., 2015;Roth and Lapata, 2016).

multi-task Hidden Features Drawing inspiration from ELmo (Peters et al., 2018), a recently proposed model for generating word representations based on bidirectional LSTms trained with a coupled language modeling objective, we extract various hidden features via multi-task learning.


The auxiliary prediction modules take − → h t pri and ← − h t pri as input.

this is partly due to the nature of the task which relies on various syntactic features, even when conceptualized as a sequence labeling task He et al., 2018b;.


(2017) reduce the resource requirement by proposing a cheap translation method, which "translates" the training data from the source to the target language word by word through a bilingual lexicon.

different from MMD, our method directly uses Euclidean distance instead of the kernel function.


More seriously, RNN-based models must give the label of "度" using only previous partial sequences "印 度 (India)", which is problematic without seeing the remaining characters.

we also compared several state-of-the-art methods on the four datasets to verify the effectiveness of our method.


The  potential words in the lexicon that match a character subsequence can be formulated as w b,e = c b , c b+1 , ..., c e−1 , c e , where the index of the first and last letters are b and e, respectively.

the state of the i-th node represents the features of the ith token in a text sequence.


That is, giving both s i,i+1 and s i,i−1 large values, so the attention head freely attends to any position without restriction of constituent prior, which is the same as the original Transformer.

the tree structures can be automatically induced from raw texts by our proposed "Constituent Attention" module, which is simply implemented by self-attention between two adjacent words.


The interpretable attention can better explain how the model processes the natural language and guide the future improvement.

giving both s i,i+1 and s i,i−1 large values, so the attention head freely attends to any position without restriction of constituent prior, which is the same as the original Transformer.


(2013) argued that a denoising autoencoder should not be too robust to the input variations as to ignore the input.

while they improve over their baseline model, their baseline model used multilayer perceptron to encode local factors, thus the encoder power is limited.


Instead of equations 19 and 24, we have: Everything else is kept the same as in the full model, expect that the size of W α and W π needs to be adjusted.

r i,0 is the probability of i-th word not being an argument of the predicate.


With NER, the spans are short and the task is less subjective than PICO or ARG, hence its higher F1 scores.

existing methods for aggregating sequence labels cannot model dependencies between the annotators' labels (Rodrigues et al., 2014;Nguyen et al., 2017) and hence do not account for their effect on annotator noise and bias.


(2) the output of word-level biLSTM (word-level), i.e., h i in Eq.


In our study, the Turkish treebank is written in extended Latin while the Kazakh treebank is written in Cyrillic.

2 for each content word, we first stochastically choose whether to replace it; then, if we have chosen to replace it, we uniformly sample the replacement word type meeting the corresponding constraints.


To solve the intractable structure, Rush and Collins (2012) decompose the structure and incorporate constraints on some composite tasks.

in this paper, we explore the first option and leverage the WALS features, which provide a reference for word order typology, to estimate the ratios.


In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially.

we can see that since the corpus linguistic statistics between Hindi and English are distinct, the baseline model only achieves low performance.


To verify this claim, we run experiments with Hebrew as the source language.

we use corpus linguistic statistics of the target languages as weak supervision signals to guide the test-time inference process when parsing with a graph-based parser.


Evaluation Metric Our evaluation metric is parsing accuracy.

some sentences generated are not semantically correct according to a human interpreter but still they preserve the sentence structure.


Though SCRF exhibits promising results in the case of longer entity recognition, it requires additional feature extractors and exhibits some limitations in the case of shorter entity recognition.

we can observe that TextCNN exhibits higher accuracy on its own classification task.


Owing to the growing popularity of deep learning techniques, a considerable number of neural network-based approaches and traditional conditional random fields (CRF) have been proposed and are widely used in many sequence labelling tasks to obtain magnificent results (Ye and Ling, 2018;Wu et al., 2018;Ghaddar and Langlais, 2018).


Furthermore, in our scientific context, we have to study two inference problems: (1) the usual search for a best parse that is required for assessing the model accuracy with known metrics, and (2) for the purpose of fMRI modelling, we also have to compute the marginal probabilities required to compute the probabilities relevant for language modelling.

let the set of transitions of a derivation be: T (y) = T (a 1 .


we have to emphasize, however, that contrary to P (a|x, y), the chosen importance distribution P * (a|x, y) locally sums to one.


We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time.


We consider a parsimonious word-selector using word embeddings (e.g., GloVe (Pennington et al., 2014)) as features to predict important words.

to previous approaches, we propose a novel framework for efficient text classification on long documents that mitigates sequential processing.


The Template + Grammar approach differs from Coherency Ranking in that all transformations are applied to every sentence instead of applying combinations of transformations and templates, which are then ranked by a language model.

rater disagreements occur most frequently when triples are ambiguous or difficult to interpret.


Our method applies group lasso to rational RNNs (Peng et al., 2018), a family of models that is closely connected to weighted finitestate automata (WFSAs).

we also take advantage of the natural interpretation of rational RNNs as "soft" patterns .


In almost all cases, our approach results in models with fewer parameters and similar or better performance compared to our baselines.

with our linear-structured wFSA, zeroing out the group associated with a state in the middle effectively makes later states inaccessible.


In this paper, we investigate three ways to perform MetaUpdate as described in the following parts.

(2019) propose to combine multi-task learning with language model pre-training and demonstrate the two methods are complementary to each other.


Experiments show that the retrofitted model significantly outperforms the original ELMo on various sentence classification and language inference tasks.

we demonstrate the effectiveness of this method applied to ELMo by a wide selection of semantic tasks.


Modeling the semantic similarity between a pair of sentences is a fundamental task in natural language processing.

treeLStM also contributes to an 1.1% increase in the F1-measure for PIt-2015, 0.9% Pearson's r for SICK, and 0.7% MAP for trecQA.


To get a dense low-dimenional representation of texts, we test seven representations covering sentence embedding, variants of un-contextualized word embedding and variants of contextualized word embedding.

for example, two systems may be close in terms of R1-f, but can be relatively distant when comparing the embedding similarities.


As far as we know, we are the first to introduce explicit loss functions to optimize the attention.

aRU is designed as an update unit based on current decoding state, aiming to retain the attention on salient parts but weaken the attention on irrelevant parts of input.


* Current affiliation: Future Corporation When incorporating pseudo data, several decisions must be made about the experimental configurations, namely, (i) the method of generating the pseudo data, (ii) the seed corpus for the pseudo data, and (iii) the optimization setting (Section 2).

for the PRETRAIN setting, we pretrain the model with Adam and then fine-tune it on BEA-train using Adafactor (Shazeer and Stern, 2018) 10 .


The classification uses features of topic posteriors: which is the proportion of the tokens assigned to topic k in document d. The baselines include polylingual tree LDA (Hu et al., 2014, ptLDA) which encodes the dictionary as a tree prior (Andrzejewski et al., 2009), Multilingual Topic Anchoring (Yuan et al., 2018, MTAnchor), and Multilingual Cultural-common Topic Analysis (Shi et al., 2016, MCTA).

our MTM has higher F1 than other models in both intra-and cross-lingual evaluations, while discovering coherent topics and meaningful topic links.


(2016) proposes data counterfeiting to reduce the complexity of transferring trained parameters across multiple domains.

p t is the probability distribution of the next token over all tokens in dictionary.


This can significantly improve the learning of style and variation in human language.

it still remains a challenge in task-oriented dialogue systems to generate truly natural utterance indistinguishable from a human's response.


• We find that the lengths and sentence patterns of the responses can be strongly correlated to each dimension of the latent variable.

i have to install the drivers for the graphics card.


Embedding metrics first map the generating responses to a vector space and then compute the cosine similarly with corresponding ground responses.

when the sentence grows longer, it has the drawback of tending to generate short and generic responses (Vinyals and Le, 2015).


The pseudo-labelling method is not able to improve performance over the baseline regardless of the amount of labelled data.

the result of the DSt joint accuracy is presented in Figure 2.


Semantic slot filling is one of the major tasks in spoken language understanding (SLU).

in ProgModel, each model M t has an independent g t which is initialized by g 0 in M 0 .


In this paper, we consider a practical setting that a batch 1 of new training data U t becomes available at each batch t. Our goal is to enable the continual learning capability of a slot filling model such that it can keep learning new utterances efficiently as well as remember old knowledge without the needs of accessing old training data.

unfortunately, existing approaches target on offline model training using a large scale training data.


Training: We implemented ProgModel model using TensorFlow 1.4.0 and conducted the experiments on NVIDIA Tesla M40.

as the size of training set grows, it becomes infeasible to repeatedly conduct time consuming retraining on such a large dataset.


For the former, we simply concatenate the contextual signals to get current turn feature vector, i.e.

for IC task and conditional random fields (CRF) for SL task (Jeong and Lee, 2008;Wang1 et al., 2002;Raymond and Riccardi, 2007), more recent approaches rely on deep neural networks to jointly model IC and SL tasks (Yao et al., 2014a,b;Guo et al., 2014;Zhang and Wang, 2016;Liu and Lane, 2016c;Goo et al., 2018).


For the more challenging follow-up turns, CASA-NLU yields significant gains over the baseline IC performance.

these systems predict the possible slotvalue pairs at utterance level (Zhong et al., 2018), making it necessary to maintain ontology of all possible slot values, which is infeasible for certain slot types (e.g., restaurant names).


Decay-hard sampling: to imitate the behavior that human gradually increase the difficulty of their learning materials at different stages, we propose a decay-hard sampling strategy which decays the margin in Equation (4) with the training process going on.

(2015) introduces a straightforward negative sampling strategy to improve the assignment of subjects and objects on a convolution neural network.


Task-oriented dialogue systems have been widely adopted in the industry (e.g., Amazon Alexa, Google Home, Apple Siri, Microsoft Cortana) as a virtual agent to tend to the needs of the users.

to address such variance in the alignment, we turn to probabilistic modeling with latent variables as it has been successfully used in several recent taskoriented dialogue systems (Wen et al., 2017;Zhao et al., 2017Le et al., 2018).


The out-of-scope queries are similar in style to the in-scope queries.

dSTC 1 (Williams et al., 2013), dSTC 2 (Henderson et al., 2014a), and dSTC 3 (Henderson et al., 2014b) contain "chatbot style" queries, but the datasets are focused on state tracking.


(2018) count most frequent characters, set a cutoff around eighty for each language, and remove sentences with illegal characters.

we take the development set as training data for our scoring system and treat the problem as a clustering task rather than a classification task.


Brian Thompson is supported through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program.


The obtained policies fall into two main categories: (1) fixed-latency policies (Ma et al., 2019;Dalvi et al., 2018) and (2) context-dependent adaptive policies (Grissom II et al., 2014;Cho and Esipova, 2016;Gu et al., 2017;Alinejad et al., 2018;Arivazhagan et al., 2019;Zheng et al., 2019a).

this greedy policy may not always be the best choice for all situations.


All heads in pr j are further concatenated as a mixed embedding m j ∈ R d model in turn.

rui Wang was partially supported by JSPS grantin-aid for early-career scientists (19K20354): "Unsupervised Neural Machine Translation in Universal Scenarios" and NICT tenure-track researcher startup fund "Toward Intelligent Machine Translation".


The reason may be that adding RPEs into variant heads can encode order dependencies based word vectors from variant vector sub-spaces, which is one of the advantages of the multi-head mechanism.

for the other setting not mentioned, we followed the setting in Vaswani et al.


This indicates that the learned RPEs are beneficial for the Transformer system.

there is a new sequence of mixed embeddings: The M is mapped to a query matrix Q M , and their keys and values matrices K M and V M .


(4), the output of each head is computed by Eq.

any word in the entire vocabulary has the same PE on the same position index.


Most noticeably, on both languages we are able to approximate the classification accuracy computed on gold standard English tweets.


Indeed, its curves in the two figures show either a stable (It-En) or even a slightly downward trend (De-En) that confirms the known limitations of NMT to preserve sentiment traits of the source sentences (see Section 1).

in doing so, they indirectly model the human-oriented quality criteria (adequacy and fluency) expressed in the training corpus.


The reported results here are obtained with a greedy parsing, where the words in the dictionary are first sorted and then applied from the top downwards.

comparing the compression capacity of models with different vocabulary size is misleading as a larger vocabulary size can obviously lead to smaller sequences.


Experimental results on NIST Chinese⇒English and WMT14 English⇒German translation tasks show that the proposed approach consistently boosts performance over both the absolute and relative sequential position representations.

several researches reveal that the sequential structure may not be sufficient for NLP tasks (Tai et al., 2015;Kim et al., 2017;Shen et al., 2019), since sentences inherently have hierarchical structures (Chomsky, 1965;Bever, 1970).


However, #5 was worse than #2, despite being trained in the same way, because #5 involves up to eight target languages, inevitably reducing the allowance for each target language.

2017to improve several monolingual NLP tasks.


IT-embed please report this bug to the developers .

domain adaptation is required when domain-specific data is scarce or nonexistent.


There are also work on adaptation via retrieving sentences or n-grams in the training data similar to the test set (Farajian et al., 2017;Bapna and Firat, 2019).

as shown in Figure 2, DaFE significantly outperforms back-translation in data-scarce scenarios, as low quality back-translated data can actually be harmful to downstream performance.


The dataset contain 2K development and test sentences in each domain, and about 715K, 1M and 337K training sentences respectively.

it can be difficult to find similar parallel sentences in domain adaptation settings.


grammar induction) does not require labeled data and can make use of large amounts of unlabeled data that are freely available.

table 2 shows the choices of O, D and R for several widely used models.


We apply these regularization method to the Convex-MST model (Grave and Elhadad, 2015).

we propose three different regularization terms to effectively encourage similarity of the two models.


In theory, sentential information is more important for ambiguous words that need to be disambiguated than non-ambiguous words.


However, concatenating forward and backward hidden states doubles the dimension.

in addition, the higher the encoder layer, the more relevant information is encoded into hidden states.


This type happens mostly in Korean compound nouns where Korean compound nouns can be written as one or more eojeols.

2 Each eojeol in the Sejong corpus is annotated with pairs of a morpheme and its POS tag.


Furthermore, this paper uses skip-connections (He et al., 2016) in order for the CRF network to pay attention to the hidden states of the decoder.

at every time t, the decoder hidden state z t is calculated from the embedding of the previous syllable s t−1 , the decoder state z t−1 , and the context vector c t .


(2018) indicated, developing a TCN architecture that only considers these two characteristics restricts how far the model can utilize previous information.

/ wuSuwla and / wuSuwlu, both mean "arrival" but have different syntactic positions), and named entities.


In languages such as Arabic and Hebrew, some vowels are not part of the alphabet and diacritics are used for vowel specification.

a-TCN was 24% faster than BiLSTM while being marginally lower by 0.2% in terms of DER.


To encourage Q-BOT to ask diverse questions, we propose a simple auxiliary loss that penalizes similar dialog state embeddings.

(2016a) using a maximum mutual information criterion.


While the former identifies a speaker's intent, the latter extracts semantic constituents from the natural language query.

in large-scale settings with millions of utterances, this would lead to costly training times, high computational requirements and optimization difficulties.


Figure 1 illustrates our gradient updates promoting diversity.

we introduce a new objective function which explicitly encourages attention diversity to learn an improved visual-semantic embedding space.


We leverage image representations extracted from a network pre-trained over large amounts of labeled data.

we choose the Vizwiz benchmark specifically because it is a more suitable benchmark for measuring transfer learning effects.


variable sized regions that are better correlated with object boundaries have gained momentum (Fu et al., 2017;Pedersoli et al., 2017;Anderson et al., 2018).


Captions were generated by humans and two caption models (11,985 instances in total).

the results of metric performance in table 1 show that overall, using the three metrics proposed in REO, especially extraness and omission, led to a noticeable improvement in Kendall tau's correlation compared to the best reported results based on prior metrics.


Following standard practice (Anderson et    Text in red is extra information, while text in green is missing information.

we measure relevance using standard cosine similarity.


Two scores, cosine similarity, and Delta-E are applied for evaluating the per-formance.

for Delta-E, the RGB model and ensemble perform better.


Parallel data The Dundee corpus (Kennedy et al., 2003) contains recordings of measurements for eye movements of ten English-speaking participants during reading 20 newspaper articles from The Independent making in total 2368 unique sentences.

then, we back-propagate the loss for the outputs associated to that treebank and ignore the rest, i.e.


subject vs. object) and use them to improve a transition-based parser, trained on a structured perceptron with discrete features (Collins, 2002;Zhang and Nivre, 2011).

1 Disjoint data Most of dependency treebanks do not contain gaze annotations.


In this paper we propose to learn to leverage eyemovement data as a sequence labeling task.

we explore the informativeness of the basic gaze features: total fixation duration on a word w (total fix dur), mean fixation duration on w (mean fix dur, num-ber fixations on w (n fix) and fixation probability on w (fix prob).


This dataset was used as the starting point to create a corpus that fits the purpose of dependency parsing, the Dundee treebank (Barrett et al., 2015).

the model predicts only 4 features that are then used as features for readability assessment.


The Upper Bound in the Induced column of these tables represents a perfect labeling of the most accurate induced binary tree from DIORA, and the Majority (NP) row is the same tree labeled with the most common tag.

while ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) do not produce phrase vectors or induce recognizable constituency parse structure, 3 we show that they can still be used for unsu- 3 BERT does not strictly output word-level vectors.


Cluster 0 includes the majority of VBZ and MD (will, won't, can, could), Cluster 1 is mainly composed of past tense VPs (VBD), Cluster 2 has many VBG, and Cluster 3 consists of 86% VBP.

these works and nearly all previous research (Klein and Manning, 2002;Seginer, 2007;Ponvert et al., 2011;Spitkovsky et al., 2013) have focused on unlabeled constituency parsing.


Finally, we use our own ConvBank dataset to fine-tune our bestperforming baseline model.

we use the UD tag flat to link repeated words and stutters to indicate that these headless multi-word expressions are a single syntactic unit.


However, models trained on datasets based on news articles and web data do not perform well on spoken human-machine dialog, and currently available annotation schemes do not adapt well to dialog data.

we also train a separate model on Tweebank v.2 (Liu et al., 2018) alone to determine how a parser trained on tweets, which are arguably more similar to conversational speech, will perform.


Dependency parsing is a well studied (Dozat and Manning, 2016;Clark et al., 2018) task.

convBank uses 32 of the 37 possible primary relations in ScUD, described above.


The model trained on EWT or Tweebank or EWT and Tweebank combined achieved similar results that are better than the model trained on ConvBank alone.

most dependency parsers are trained on wellwritten text datasets, such as news articles and web-scrape data.


, x n−1 ) with n tokens, the task is to predict a TOP parse tree, as illustrated in Figure 1.

our parser is based on a joint distribution over the chain at each span.


(2018) shows that the span embedding of x i:j is powerful enough to identify the parent label π[i, j] with high accuracy.

most utterances have nested intents, with some utterances containing intents from different domains.


Our span-based models can be trained in a highly parallelized fashion without having to run the computationally expensive decoder.

to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy.


The training objective of document-level NMTs is to maximize the log-likelihood of translations in document context as following: where D is the training set of the DocNMT.

we also investigate the effect of the hyper-parameter of QCN: the number of feature capsules.


Despite the great success of the above models, they are designed for sentence-level transla-Joint work with Pattern Recognition Center, WeChat AI, Tencent Inc, China.

the equation of multi-head attention computing procedure is as following: where W i Q , W i K and W i V are the parameter matrices, Q, K and V indicates the query, key and value representations.


This selfcontained adapter module can be injected between any two layers of the network, without disrupting the original operation.

when using adapters for domain adaptation, we follow the following two step approach: • Pre-training: Pre-train the NMT model on a large open-domain corpus.


They introduce domain specific gates that control the contribution of hidden units feeding into the next layer.

for multilingual NMT, we follow the following two step approach: • Global training: Train a fully shared model on all language pairs, with the goal of maximizing transfer to low resource languages.


Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains.

this highlights a key strength of the approach: by varying adapter capacity it is possi-ble to adapt the same model to domains of varying complexity and amounts of data.


Table 5 illustrates simplification operations observed for a fixed grade 12 Spanish input into English with target grade levels ranging from 9 to 3.

annotating all data  with ARI grades, including the OPUS parallel corpus, hurts BLEU.


Recent work on interpretability for NLU tasks uses methods such as diagnostic tasks (Belinkov et al., 2017;Tenney et al., 2019;, attention based methods (Raganato and Tiedemann, 2018) or task analysis (Zhang and Bowman, 2018) and is primarily focused on understanding the linguistic features encoded by a trained model.

due to the differing nature of translating multiple languages to English and vice versa, the representation space of these two sets of languages, All-to-English and English-to-Any, behave differently and separate quite clearly ( Figure 11 in the Appendix).


This might also be motivated by training on a shared target language (En).

we express a layer's representations as its activations on a set of n examples, X = {x 1 , .


We find that representations of high resource languages are quite robust to fine-tuning on any language.

we see in both Figure  3 (middle left of plot) and Figure 4 (bottom left of plot) that they are each others' closest neighbors.


(2019) extend previous one-type answer prediction (Seo et al., 2017) to multi-type prediction that supports span extraction, counting, and addition/subtraction.

an arithmetic expression that has a number as the final answer can be obtained and easily evaluated.


answers has accepted answers but it does not contain labeled duplicate questions.

besides supervised training, this also includes unsupervised training with the encoder-decoder architecture.


A counterexample is Quora, which only contains question titles.

in this work, we propose two novel methods for scenarios where we only have access to unlabeled questions (title-body), including (1) automatic duplicate question generation (DQG); and (2) weak supervision with the title-body pairs (WS-TB).


One of the biggest advantages of our proposed methods is that they can use larger amounts of training data.

we can add more instances to the training splits with these methods.


These tasks can naturally be integrated into existing KBQA systems.

since clarification question generation and clarification-based question answering are built upon ambiguous situations, they only use the positive data in their training, development, and test sets.


BLEU evaluates the generated text by computing its similarity with the gold text.

based on whether the inter-relation between different source information is explicitly explored, the classification models can be classified into two categories, unstructured models and structured models.


There is a large body of work on multi-view word embeddings (see Section 2.3), including domain adapted word embeddings (Sarma et al., 2018).

we also replace GCCA with naive view concatenation or view averaging.


Indeed, as Figure 1 suggests, current popular tools for toxic language detection do not recognize the toxicity of MAS and further, sentiment tools can label these comments as being positive.

some are formatted to show the conversation structure of the recollection of the incidents, while some others are shown as plain narratives.


To the best of our knowledge, we are the first to consider using metric learning to learn embeddings from episodes of user activity.

method mRR (⇑) mR (⇓) R@1 (⇑) R@2 R@4 R@8 R@16 R@32    For certain tasks it is useful to identify groups of accounts shared by the same author or to identify groups of accounts behaving in a similar fashion (Solorio et al., 2013;Tsikerdekis and Zeadally, 2014).


This paper describes a novel approach to learning such embeddings using metric learning coupled with a novel training regime designed to learn invariant user representations.

we optionally apply dropout at this stage if training.


In this paper, we propose a large scale, humanannotated, parallel stylistic dataset called PAS-TEL, with focus on multiple types of personas in conjunction.

table 6 shows our results on style tranfer.


We define a task called controlled style classification where all other style variables are fixed 5 , except one to classify.

if annotators are asked to write their own text given a reference sentence, they may simply produce arbitrarily paraphrased output which does not exhibit a stylistic diversity.


Recent years have seen increased interest in the automatic analysis of long and complex narratives.

the enriched model with the context interaction layer is illustrated in Figure 2a.


As illustrated in Figure 2, given a sentence A = {w 1 , w 2 , .., w n } written by a user in Tree Hole, where n is the length of a sentence, the aim of suicidal expression classification is to classify whether this sentence contains expression about suicidal ideation or not.

we feed the word embeddings X into the LSTM as following: (1) where h t−1 , h t represent the hidden states at time t − 1 and t, H a = {h 1 , h 2 , ..., h n } ∈ R n×de is sentence representation of A, [k 1 , k 2 ] stand for the possibility of whether the sentence contains expression about suicidal ideation or not.


By transferring latent dialogue knowledge from multiple sources of varying generality, we obtained a model with superior generalization to an underrepresented domain.

it can be seen that few-shot models with LAED representation are the best performing models for this objective.


For the NLU ZSDG setup, we annotated all available SMD data and randomly selected a subset of 1000 utterances from each source domain, and 200 utterances from the target domain.

the ZSDG setup used approximately 150 training utterance-annotation pairs for each domain, including the target one, totalling about 450 annotated utterances.


When testing, we repeat 10 epochs and take the average.

as shown in Section 4.3, we use this system to pre-train Full-S. • MP-S: a neural dialogue system which uses message passing to infer dialogue states but uses a flat policy to unfold dialogues.


This is because simulated applicants tend to forge personal information about "School" and "Company" for a larger loan.

(2) We point out three major challenges of identity fraud detection and propose corresponding solutions.


(3) Experiments have shown that our approach can detect identity fraud effectively.

full-S notices the applicant gives the correct answer to a question that is hard to answer for fraudsters.


Given the same or similar dialog context, there may exist many valid expressions for the responses with the same dialog act a, each corresponding to a certain configuration of z.

instead of specifying a dialog act manually, the most appropriate one can be decided automatically.


Therefore, the text convolutional network proposed in (Kim, 2014) is leveraged to mine the relationship between expression patterns and responses.


LAED: what city would you like to know the weather for?

when confronting conversations with diverse topics or themes, SEQ2SEQ is usually prone to make generic meaningless responses due to its oversimplified parameterization.


We conduct a series of experiments that demonstrate the effects of our framework in both the evaluation of recommendation and dialog generation.

our proposed method performs the best in all evaluations compared with the baselines.


Direction and distance from the model prediction (center black dot) roughly correspond to contents and style intensity, respectively, illustrated by examples taken from Table 2. linguistic style of each other, sometime even regardless of their intentions.

our contribution can be summarized thus: 1) We introduce an end-to-end approach that generates style-specific responses from conversational data and non-parallel non-conversation style data.


The iven context may or may not be in the target style.

the highest score for generative models in each column is in bold for each target.


Since the contents and formats of these are quite different from conversation data, existing approaches tend to generate responses that are either less style-specific (Luan et al., 2017) or less context-relevant (Niu and Bansal, 2018).

the style intensity of MTask outputs rises only slightly as ρ increases.


As shown in Figure 1, annotators first discern whether the original utterance (the fifth utterance in conversation) omits concepts or entities made in previous utterances.

there are three choices for the annotators: better, similar or worse.


Each word in previous utterances will be determined to be positive P (should be identified as an omitted word) or negative N (not an omitted word).

one type of NSU that draws extra attention is ellipsis (Merchant, 2016;Kenyon-Dean et al., 2016;McShane and Babkin, 2016).


BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004) are commonly used metrics in machine translation and summarization.

pick: inspired by recent advances in transfer learning for language representation, we fine-tune the pre-trained deep bidirectional transformers for language understanding model (BERT) (Devlin et al., 2019) as a classifier to select omitted words from previous utterances.


The results prove that the proposed dataset and approach significantly improve the response quality.

the PAC model picks the correct words from previous utterances and generates a more fluent utterance.


The proposed corpus and model can motivate the future research directions 1 .

tAware and Qadpt predict more correct knowledge entities but tend to diminish generic words.


The generated skeletons are extracted from their corresponding responses with some deliberate disturbance.

many of the generated responses (e.g., "I don't know" and "I think so") appear to be generic and dull (safe response problem) (Li et al., 2016a).


Considering the fact that WoZ2.0 is a relatively small dataset, this small difference does not represent a significant big performance drop.

in real-world scenarios, it is often not practical to enumerate all possible slot value pairs and perform scoring from a large dynamically changing knowledge base (Xu and Hu, 2018).


We can see that on both tasks, S2S-Temp outperforms all baseline models in terms of all metrics, and the improvements are statistically significant (t-test with p-value< 0.01).

besides, we extract 1 million tweets with positive sentiment from a public corpus (Cheng et al., 2010) as an unpaired dataset.


In addition to the paired data, we crawl 776k questions from Zhihu, a Chinese community QA website featured by high-quality Algorithm 1 Learning a generation model with paired and unpaired data.

we have to face the data sparsity problem, as long as we attempt to create a generation model with constraints on responses.


The Seq2seq baseline tends to generate less informative responses.

we also propose to use a two-stage sampling approach to enable efficient selection of diverse variables from a large latent space, which is very essential for our model.


The latent variable z is thus set to follow a categorical distribution with each dimension corresponding to a word in the vocabulary.

the vo-cabulary consists of words that are similar in syntactic or semantic.


A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables.

they use a latent word in a factorized encoder-decoder model, but our model uses it to construct a discrete CVAE and our optimization algorithm is entirely different from theirs.


The Effect of vMF on KL Besides the metricbased performance, we also evaluate the effectiveness of different settings in sloving the latent space futility problem.

(3) To figure out this special phenomenon, we conduct an experiment on SSVN Gau composed by Gau-Extractor and Gau-Generator.


However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.

our second dataset is Ubuntu Dialogue Corpus 3 (Lowe et al., 2015).


We also extract entity-level features (e.g., directors, actors, year) using the MovieWiki dataset (Miller et al., 2016) (See Figure 1).

note that only the full supervised model (+Decide) and the fine-tuned model (+Plan) can appropriately operate every function required of an expert agent such as producing utterances, recommending items, and deciding to speak or recommend.


5 In each turn, if the user question can be answered by a SQL query, they will be shown the result table, and the expert will write an NL response interpreting the executed SQL query based on their understanding of the user's query intent (Figure 8 Appendix).

coSQL is meant to be used as the first benchmark for building general-purpose DB querying dialogue systems in arbitrary domains.


The input of the modified model is a user utterance and the related column names.

after annotation and reviewing, 3,007 of them were finished and kept in the final dataset.


The label number prediction module is similar to the column number prediction module in SQLNet.

all NL utterances in ATIS and SParC can be mapped to SQL.


Task-oriented dialog systems Task-oriented dialog systems (Henderson et al., 2014;Mrkšić et al., 2017;Budzianowski et al., 2018) have attracted increasing attention especially due to their commercial values.

the goal of dialogue-based NLIDB systems is to support general-purpose exploration and querying of databases by end users.


DailyDialog and SwDA, and provide evidence for the first hypothesis outlined above.

whereas, in our experiments, we have found that the model that uses the dialogue acts of both context and response outperforms the models that use the dialogue acts of either context or response.


Ubuntu and OpenSubtitles, do not have dialogue act annotations, and the MRDA corpus is too small, it has only 51 conversations.

for response selection, there are two frameworks that are popular in the literature; one is generative and the other is discriminative.


Therefore, the most ideal evaluation should be conducted by human.

motivated by this, we tested the RL systems trained with various user simulators on Amazon mechanical Turk (AmT) (miller et al., 2017), and asked Turkers to interact with the system and obtained their opinions.


Retrieval-based methods surpass other methods in diversity, but it is not as good in fluency, while template-based methods outperform in fluency but suffer on diversity as expected.

we first asked human to read the simulated dialogs and rate the user simulator's performance directly.


Suppose that the num-ber of input modalities is n, I l denotes the features of l-th modality, the space of I l is R d l ×t l where t l denotes the temporal length and d l denotes the feature dimensions.

when we combine them, the performances are significantly improved on metrics, especially ROUGE and CIDEr.


The goal of our work is to deal with unpaired image-caption data for image captioning.

to compute the cross entropy loss, we use the pseudo-label assignment for the Unlabeled-COCO images.